NEW ASSESSMENT PREDICTION MODEL OF ECLIPSE REPORTS BASED STATISTICAL ALGORITHMS



Authors

  • 1Bikka Venkata Pranay Kumar Reddy 2Dr. Y. Chitti Babu 3Ramesh Kunchala

DOI:

https://doi.org/10.15282/jmes.17.1.2023.10.0759


Keywords:

Natural Language Processing, Machine comprehension, Deep learnin. Machine Learning, ML, Software Bug, Bug Priority, Bug Detection, Software Security.


Abstract

The rapid growth of software scale and complexity, a large number of bug reports are submitted to the bug tracking system. In order to speed up defect repair, these reports need to be accurately classified so that they can be sent to the appropriate developers. Software fault prediction is a vital and helpful technique for boosting the quality and dependability of software. There exists the prospective to enhance project management by proactively estimating prospective release delays and implementing cost-effective measures to boost software quality. The subsets of queries extracted and then each model was analyzed how it deals with specific group of queries. The aim is to build a tool that automatically classifies software bugs according to the severity and priority of the bugs and makes predictions based on the most representative features and bug report text. We present a machine learning based solution for the bug assignment problem. We build component classifiers using a multi-layer Neural Network based on features that were learned from data directly. A hierarchical classification framework is proposed to address the mixed label problem and improve the prediction accuracy. The features are used K-Nearest Neighbor, Naive Bayes, Logistic Regression, Support Vector Machine and Random Forest) show that our proposed method achieves better performance The implementation of this study makes use of methods from AI, along with data mining, and Machine Learning, along with statistical algorithms, and also modeling. Prediction models can be of assistance in maximizing all of the resources needed for the research.



Published

2024-06-30

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